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what the Deep learning and quantum computer

9 cited papers · March 25, 2026 · Powered by Researchly AI

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Deep learning (DL) and quantum computing represent two powerful computational paradigms that researchers are increasingly combining into hybrid frameworks. Deep…

Deep learning (DL) and quantum computing represent two powerful computational paradigms that researchers are increasingly combining into hybrid frameworks.¹²

Deep learning allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction, dramatically improving performance across speech recognition, visual object recognition, and object detection. Current quantum hardware operates in the Noisy Intermediate-Scale Quantum (NISQ) era, where devices with 50–100 qubits show promise but are limited by noise and short coherence times. Preskill (2018)

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Quantum agents in the Gym: a variational quantum algorithm for deep Q-learningAndrea Skolik, S. Jerbi et al.2021Quantum

2

Representation learning with parameterised quantum circuits for advancing speech emotion recognition.Rajapakshe Thejan, Rana Rajib et al.2025Scientific reports

Deep Learning — Computational models with multiple processing layers that learn hierarchical data representations, achieving breakthroughs in speech, vision, and other domains.

¹

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Deep LearningYann LeCun, Yoshua Bengio et al.2015Nature

NISQ Quantum Computing — Quantum devices with 50–100 qubits capable of surpassing some classical tasks, but limited by gate noise, short coherence times, and high error rates.

²³Preskill (2018)²

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Quantum Computing in the NISQ era and beyondJohn Preskill2018Quantum

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Noisy intermediate-scale quantum algorithmsKishor Bharti, Alba Cervera-Lierta et al.2022Reviews of Modern Physics

Parameterized Quantum Circuits (PQCs) — Variational quantum circuits embedded within classical optimization loops, enabling quantum-enhanced learning while mitigating current hardware constraints.

⁴

Skolik et al. (2021)

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Representation learning with parameterised quantum circuits for advancing speech emotion recognition.Rajapakshe Thejan, Rana Rajib et al.2025Scientific reports

Hybrid Quantum–Classical Frameworks — Architectures that integrate quantum circuits with classical neural networks to leverage quantum properties such as superposition and entanglement for enriched feature representations.

⁴

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Diagram

 Classical Input Data
 |
 v
 [Classical Preprocessing / CNN Layers]
 |
 v
 [Quantum Encoding Layer]
 (Superposition + Entanglement)
 |
 v
 [Parameterized Quantum Circuit (PQC)]
 (Variational / Ansatz)
 |
 v
 [Quantum Measurement / Readout]
 |
 v
 [Classical Optimization Loop]
 (Gradient updates, error mitigation)
 |
 v
 Output / Decision

Table

Feature	Classical Deep Learning	Hybrid Quantum–Classical DL
Core Unit	Artificial neurons / layers	PQCs + classical layers
Training	Backpropagation	Variational optimization + classical loops
Hardware	GPUs/TPUs	NISQ quantum devices + classical CPUs
Key Advantage	Mature, scalable	Potential quantum speedup, richer representations
Key Limitation	Data/compute hungry	Noise, limited qubits, coherence issues

Quantum-enhanced hybrid deep reinforcement learning has demonstrated 95.4% decision accuracy versus 82.1% for classical methods, with a 2.8-fold acceleration in convergence speed.¹Nan et al. (2025)¹Hybrid quantum–classical machine learning frameworks that leverage parameterized quantum circuits within classical optimization loops provide a practical pathway toward near-term quantum advantage.²³V. (2026)²

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Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making.Cai Nan, Zhao Minghui et al.2025Scientific reports

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Hybrid Quantum–Classical Learning Approaches for Scalable Optimization Beyond NISQ LimitationsChatta Balaji, Dr. C. Nagesh, V. K. Pavani, Dr. V.2026International Journal of Advanced Research in Science Communication and Technology

3

Quantum agents in the Gym: a variational quantum algorithm for deep Q-learningAndrea Skolik, S. Jerbi et al.2021Quantum

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Current NISQ devices suffer from limited qubit counts, short coherence times, and high error rates, making fully fault-tolerant quantum algorithms impractical in the near term.¹²V. (2026)¹

It remains an open question whether variational quantum algorithm (VQA)-based approaches can be competitive with state-of-the-art classical neural networks even on simple benchmark tasks. Skolik et al. (2021)

1

Hybrid Quantum–Classical Learning Approaches for Scalable Optimization Beyond NISQ LimitationsChatta Balaji, Dr. C. Nagesh, V. K. Pavani, Dr. V.2026International Journal of Advanced Research in Science Communication and Technology

2

Noisy intermediate-scale quantum algorithmsKishor Bharti, Alba Cervera-Lierta et al.2022Reviews of Modern Physics

Deep learning enables hierarchical representation learning across diverse domains including speech and vision.

¹

NISQ-era quantum computers show promise but are constrained by noise and limited qubit counts, requiring hybrid approaches.

²³Preskill (2018)²

PQCs embedded in classical optimization loops form the backbone of hybrid quantum–classical machine learning frameworks.

⁴V. (2026)³

Quantum-enhanced reinforcement learning has demonstrated measurable accuracy and convergence improvements over classical baselines.

⁵Nan et al. (2025)⁵

Quantum neural networks trained using fidelity as a cost function show efficient training with reduced memory requirements. Beer et al. (2020)

1

Deep LearningYann LeCun, Yoshua Bengio et al.2015Nature

2

Quantum Computing in the NISQ era and beyondJohn Preskill2018Quantum

3

Hybrid Quantum–Classical Learning Approaches for Scalable Optimization Beyond NISQ LimitationsChatta Balaji, Dr. C. Nagesh, V. K. Pavani, Dr. V.2026International Journal of Advanced Research in Science Communication and Technology

4

Representation learning with parameterised quantum circuits for advancing speech emotion recognition.Rajapakshe Thejan, Rana Rajib et al.2025Scientific reports

5

Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making.Cai Nan, Zhao Minghui et al.2025Scientific reports

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"Variational quantum eigensolver (VQE) for optimization problems in machine learning"
"Quantum advantage benchmarks for hybrid classical-quantum neural networks"
"Error mitigation strategies for parameterized quantum circuits in NISQ devices"

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